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ThruPLEX Tag-seq for confident detection of low-frequency variants. ThruPLEX Tag-seq kit
Tech Note

ThruPLEX Tag-Seq detection sensitivity and specificity using Horizon cfDNA reference standards

Data and bioinformatics support kindly provided by:

Jinglan Zhang and Hongzheng Dai at Baylor Miraca Genetics Laboratories, Houston, Texas, United States
Richard Yim at University College London, London, United Kingdom
Shawn Quinn at Curio Genomics, Dexter, Michigan, United States

Introduction  

The ThruPLEX Tag-Seq Kit is a next-generation sequencing (NGS) library preparation kit designed to distinguish low-frequency variants in plasma, tissue, and formalin-fixed paraffin-embedded (FFPE) samples. The kit uses our proprietary ThruPLEX chemistry to prepare molecularly tagged and sample-indexed Illumina NGS libraries from cDNA, cell-free DNA (cfDNA) and fragmented double-stranded DNA (dsDNA) (Figure 1).

 ThruPLEX Tag-seq technology

Figure 1. ThruPLEX technology. A 3-step, single-tube reaction that starts with cDNA, cfDNA, or fragmented dsDNA. The ThruPLEX stem-loop adapters with molecular tags and blocked 5’ termini are blunt-end ligated to the repaired input DNA. These molecules are then extended and amplified using a high-fidelity polymerase to yield a molecularly tagged and sample-indexed Illumina NGS library.

ThruPLEX Tag-Seq features an extremely simple and fast workflow that can be completed in approximately 2 hours. It requires no intermediate purification or sample transfer steps (Figure 2), which are known to decrease library yield and increase the probability of contamination and handling errors.

Single-tube workflow for library preparation using ThruPLEX Tag-seq

Figure 2. ThruPLEX Tag-Seq single-tube library preparation workflow. The ThruPLEX Tag-Seq workflow consists of three simple steps that take place in the same well or PCR tube, eliminating the need to purify or transfer the sample material.

When combined with hybridization-based target enrichment, ThruPLEX Tag-Seq libraries can be sequenced at great depth (5,000–10,000X raw coverage) to confidently detect variants present at low frequencies with only 1–50 ng of input DNA. ThruPLEX Tag-Seq provides more than 16 million random molecular tags to uniquely label the input DNA fragments (Figure 3). The unique molecular tags (UMTs) are incorporated during the ligation step, allowing subsequent computational correction of errors accumulated during amplification and sequencing. The construction of consensus sequences of molecular duplicates during bioinformatic analyses increases the accuracy of sequencing individual input molecules, enabling the detection of low-frequency variants with high sensitivity and specificity.

Diagram of DNA template with UMTs

Figure 3. ThruPLEX Tag-Seq provides 16 million unique combinations of molecular tags. More than 4,000 unique molecular tags (UMTs) are available on each side of the insert (Panel A). After sequencing, mapping, and counting of the reads, more than 99% of the UMT counts are distributed within 2-fold of the mean, illustrating unbiased incorporation (Panel B).

To validate the increased accuracy of variant detection using the ThruPLEX Tag-Seq Kit, we measured the limits of variant detection using reference cfDNA engineered with variants at different allele frequencies. This effort involved the steps of library preparation, target enrichment, sequencing, and data analysis (Figure 4). It demonstrates the utility of ThruPLEX Tag-Seq as a powerful tool for the detection of rare alleles.

Complete workflow of Thruplex Tag-seq from sample to data analysis

Figure 4. Complete workflow from sample to data analysis.

Results  

Using the Curio bioinformatics platform, ThruPLEX Tag-Seq's UMT reads were grouped into amplification families and then collapsed into family consensus sequences that represent unique DNA molecules. Reads were discarded if the amplification family consisted of fewer than 2 members. Sequencing positions were called as ambiguous (N) if fewer than 60% of the reads at the position in the amplification family had the identical sequence. This process removes errors generated during library preparation, sequencing, and base-calling.

Figure 5 illustrates the power of using ThruPLEX Tag-Seq to reduce background errors for more confident identification of variants. The aligned raw reads show that the expected EGFR T790M mutation (yellow dot) is obscured by false-positive noise (grey dots) in the 130-bp window centered at the mutation position, making it difficult to distinguish the true mutation from false positives (Figure 5, Panel A). In contrast, using family consensus reads, the consensus sequence shows a dramatic reduction in the level of background errors (Figure 5, Panel B). This error reduction provides a clear separation of the true mutation from the noise.

The signal-to-noise ratio was calculated for each sample by processing the data after removing duplicate reads or by constructing consensus reads using UMTs. For each sample and processing method, the signal was calculated by taking the average of the allele frequency detected at six expected mutation positions and the noise was calculated by averaging the allele frequency detected outside the six positions across the entire captured region. The results (Figure 5, Panel C) show a 3X to 6X improvement in signal-to-noise ratio when UMTs were utilized for error correction and consensus building during data processing.

Reduced background signal and improved signal-to-noise ratio using UMTs.

Figure 5. UMTs greatly reduce background signal. Data from the same sample were processed without (Panel A) or with UMTs (Panel B). Panel A. In conventional library preparation, the true mutation (yellow dot) can be difficult to detect due to background errors (gray dot). Panel B. With the use of UMTs, the background is significantly reduced, and the true mutation can be detected. Panel C. UMTs greatly increase the signal-to-noise ratio: The signal-to-noise ratio is significantly improved at various allele frequencies between conventional library preparation (gray) and library preparation containing UMTs (yellow).

Overall, we tested six Horizon cell-free DNA reference standards ranging from 0% (wild type) to 5% minor allele frequency (MAF). Table I shows that all six variants were called at their expected frequencies, with 100% sensitivity and per-base specificity greater than 99.6%. By combining deep sequencing with the ThruPLEX Tag-Seq Kit, it was possible to detect mutations present at 0.5% allele frequency using a starting input of just 10 ng of DNA. Lower detection limits and higher specificity can be achieved, depending on sample quality, input amount, capture efficiency, sequencing depth, and data processing algorithms. Horizon Multiplex I cfDNA Reference Standards (Horizon HD780) were used as-is or titrated using the wild-type reference standard to generate samples at additional allele frequencies. Variants were detected at their expected frequencies (MAF) with high sensitivity and specificity.

DNA
Input

 Enrichment Sequencing Sample
MAF		 Detected variant frequency Sensitivity Specificity
EGFR
L858R		 EGFR
T790M		 KRAS
G12D		 NRAS
A59T		 NRAS
Q61K		 PIK3CA
E545K
30 ng 110 kb panel,
Agilent
SureSelect		 ~1,000X
mean unique coverage,
NextSeq® 500		 5% 3.7% 5.7% 6.1% 4.4% 5.9% 5.6% 100.0% 99.8%
1% 0.5% 1.4% 1.5% 1.7% 1.2% 1.0% 100.0% 99.9%
WT** 0%* 0%* 0%* 0%* 0%* 0%*  
10 ng 240 kb panel,
Roche
NimbleGen		 ~500X
mean unique coverage,
HiSeq® 2500		 2.5% 2.3% 1.0% 2.5% 3.6% 2.3% 1.6% 100.0% 99.6%
1% 1.4% 0.6% 1.3% 0.9% 0.4% 1.7% 100.0% 99.8%
0.5% 1.4% 0.2% 0.9% 0.8% 1.3% 1.1% 100.0% 99.8%

*Not detected
**100% wild-type negative control

Table I. Using the ThruPLEX Tag-Seq kit enables detection of mutations with as little as 0.5% allele frequency.

Conclusions  

Using 10 ng and 30 ng of Horizon reference standards, we prepared molecularly-tagged libraries using the ThruPLEX Tag-Seq Kit and performed targeted sequencing to a mean raw coverage of 5,000X. Data processing and analysis were conducted using the ultra-fast Curio bioinformatics platform. We demonstrated that ThruPLEX Tag-Seq libraries can be used to detect variants at 0.5% allele frequency with high sensitivity and specificity, using just 10 ng of input DNA.

Equipped with more than 16 million unique molecular tags, ThruPLEX Tag-Seq is a powerful tool for the confident detection of low-frequency alleles. ThruPLEX Tag-Seq's highly efficient chemistry and single-tube workflow work together to preserve molecular complexity, allowing researchers to discover more information from precious samples using just 1 to 50 ng of DNA.

Researchers have the freedom to use any commercially available capture panels. Alternatively, they can design custom capture panels to interrogate genomic regions of interest that span hundreds of genes and study variants present at low allele frequencies. Lower detection limits can be achieved, depending on sample quality and input amount, capture efficiency, and sequencing depth.

Methods  

Horizon reference standards

Horizon Multiplex I cfDNA Reference Standards (Horizon HD780) are derived from human cell lines with six engineered single-nucleotide variants (SNVs). The DNA has been fragmented to an average size of 160 bp to resemble cfDNA extracted from human plasma and is provided with allelic frequencies measured by digital PCR. To generate samples with lower allele frequencies, the Horizon rare-allele samples were diluted with the Horizon wild-type reference DNA.

Library preparation

Molecularly-tagged and sample-indexed libraries were prepared from 10 ng or 30 ng of Horizon HD780 DNA using the ThruPLEX Tag-Seq Kit with dual indexes. Amplified libraries were purified using AMPure XP beads and eluted in nuclease-free water for enrichment.

Target enrichment

Hybridization and capture of the pooled, indexed ThruPLEX Tag-Seq libraries were carried out using the SureSelect XT2 or Roche NimbleGen SeqCap EZ target enrichment systems, using a 120-kb or 240-kb custom panel. Multiple libraries were pooled for each hybridization reaction. In addition, 1 μl (1 nmol) each of i5 and i7 xGen Universal Blocking Oligo - TS HT (Integrated DNA Technologies) were added into the hybridization reaction to prevent nonspecific binding.

Sequencing

Target-enriched libraries were sequenced on a HiSeq® 2500 or NextSeq® 500 system to achieve a raw coverage of 5,000X.

Data analysis

Data processing and analysis were performed on the Curio Genomics bioinformatics platform. FASTQ files were uploaded to Curio, and the sequencing data was aligned with unique molecular tag processing. Variant analysis was conducted using the Curio variant detection module.

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Notice to purchaser

Our products are to be used for Research Use Only. They may not be used for any other purpose, including, but not limited to, use in humans, therapeutic or diagnostic use, or commercial use of any kind. Our products may not be transferred to third parties, resold, modified for resale, or used to manufacture commercial products or to provide a service to third parties without our prior written approval.

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Notice to purchaser

Our products are to be used for Research Use Only. They may not be used for any other purpose, including, but not limited to, use in humans, therapeutic or diagnostic use, or commercial use of any kind. Our products may not be transferred to third parties, resold, modified for resale, or used to manufacture commercial products or to provide a service to third parties without our prior written approval.

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Notice to purchaser

Our products are to be used for Research Use Only. They may not be used for any other purpose, including, but not limited to, use in humans, therapeutic or diagnostic use, or commercial use of any kind. Our products may not be transferred to third parties, resold, modified for resale, or used to manufacture commercial products or to provide a service to third parties without our prior written approval.

Documents Components Image Data

See what our customers are saying about ThruPLEX Tag-seq technology!

"ThruPLEX Tag-seq was easy to use with its simple and straightforward protocol. The unique molecular tags reduced the false positive variant calls and enabled accurate detection of true mutations present at low frequencies."
—Jinglan Zhang, Ph.D., Technical Director for NGS, BAYLOR MIRACA GENETICS LABORATORIES

Using Unique Molecular Identifiers (UMIs) in NGS experiments

Learn more about using unique molecular identifiers in your research for additional quality control of your PCR results.

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